1. Technical Field
The present teaching relates to methods, systems, and programming for Internet services. Particularly, the present teaching is directed to methods, systems, and programming for evaluating query suggestions quality.
2. Discussion of Technical Background
Online content search is a process of interactively searching for and retrieving requested information via a search application running on a local user device, such as a computer or a mobile device, from online databases. Online search is conducted through search engines, which are programs running at a remote server and searching documents for specified keywords and return a list of the documents where the keywords were found. Known major search engines have features called “search suggestion” or “query suggestion” designed to help users narrow in on what they are looking for. For example, as users type a search query, a list of query suggestions that have been used by many other users before are displayed to assist the users in selecting a desired search query.
However, query suggestions provided by existing search engines are limited to search terms (keywords) that are mined from query logs. Query suggestions are often ranked based on popularity of each mined search term in search history with respect to general user population. Moreover, there lacks an efficient and effective way to measure the precision of entity tagging and ranking in query suggestion. Traditionally, precision can only be measured using editorial judgments or other methods of gathering ground truth, which is both costly and often introduces extensive latencies. Also, editorial judgment or ground truth-based precision measurement methods are limited to a set of sample entities, rather than the entire reachable portion of the query suggestion system.
Therefore, there is a need to provide an improved solution for evaluating query suggestions quality to solve the above-mentioned problems.